Spectral Learning with Type-2 Fuzzy Numbers for Question/Answering System.
Bibliographic record
Abstract
Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natural language processing. They rely on graphs that jointly represent each data point. The problem of how to best formulate the graph representation remains an open research topic. In this paper, we introduce a type-2 fuzzy arithmetic to characterize the edge weights of a formed graph as type-2 fuzzy numbers. The fuzzy numbers are identified by the changing parameters of the fuzzy kernel nearest neighbor algorithm, namely the degree of fuzziness and the hyper-parameter of the Gaussian kernel function, both of which have an effect on the uncertainty in forming the affinity matrix of the graph. We introduce a new graph-based semi-supervised learning with the type-2 arithmetic operations. We apply this technique in the framework of label propagation and evaluate on a question answering task. We demonstrate that the type-2 SSL can improve the prediction accuracy and can be considered to be the an alternative tool for text mining applications of computational linguistics. Keywords—Graph-based semi-supervised learning, kernel fuzzy k-nearest neighbor, type-2 fuzzy numbers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".